Hi I'm Abdullah

Covid_19 Symptoms Using for Result Machine Learning & 98%

*Import Libraries

pip install dataprep

Collecting dataprep
  Using cached dataprep-0.4.5-py3-none-any.whl (9.9 MB)
Collecting wordcloud<2.0,>=1.8
  Using cached wordcloud-1.8.2.2-cp39-cp39-win_amd64.whl (153 kB)Note: you may need to restart the kernel to use updated packages.

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
anaconda-project 0.10.2 requires ruamel-yaml, which is not installed.
jupyter-server 1.13.5 requires pywinpty<2; os_name == "nt", but you have pywinpty 2.0.2 which is incompatible.
distributed 2022.2.1 requires dask==2022.02.1, but you have dask 2022.11.1 which is incompatible.

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Installing collected packages: jinja2, Werkzeug, regex, ply, flask, dask, wordcloud, varname, sqlalchemy, scipy, rapidfuzz, python-stdnum, python-crfsuite, pydot, pydantic, metaphone, jsonpath-ng, flask-cors, dataprep
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Successfully installed Werkzeug-2.2.2 dask-2022.11.1 dataprep-0.4.5 flask-2.2.2 flask-cors-3.0.10 jinja2-3.0.3 jsonpath-ng-1.5.3 metaphone-0.6 ply-3.11 pydantic-1.10.2 pydot-1.4.2 python-crfsuite-0.9.8 python-stdnum-1.18 rapidfuzz-2.13.2 regex-2021.11.10 scipy-1.9.3 sqlalchemy-1.3.24 varname-0.8.3 wordcloud-1.8.2.2

import pandas as pd
import numpy as np

# data visualization library 
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(context='notebook', style='darkgrid', palette='colorblind', font='sans-serif', font_scale=1, rc=None)
matplotlib.rcParams['figure.figsize'] =[6,6]
matplotlib.rcParams.update({'font.size': 15})
matplotlib.rcParams['font.family'] = 'sans-serif'
# dataprep

# dataprep
from dataprep.eda import *
from dataprep.eda.missing import plot_missing
from dataprep.eda import plot_correlation

Data Analysis

covid = pd.read_csv('covid 19.csv')
covid

	S.No	Fever	BodyPain	Age	RunnyNose	DiffBreath	InfectionProb
0	1	102	0	9	0	-1	0
1	2	102	0	10	0	0	1
2	3	104	0	33	1	-1	0
3	4	101	1	59	0	1	0
4	5	99	0	98	0	0	0
...	...	...	...	...	...	...	...
2570	2571	99	0	90	0	0	1
2571	2572	100	0	53	0	-1	1
2572	2573	101	0	44	1	0	0
2573	2574	102	0	97	0	-1	1
2574	2575	104	1	62	1	-1	1

2575 rows × 7 columns

covid.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2575 entries, 0 to 2574
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype
---  ------         --------------  -----
 0   S.No           2575 non-null   int64
 1   Fever          2575 non-null   int64
 2   BodyPain       2575 non-null   int64
 3   Age            2575 non-null   int64
 4   RunnyNose      2575 non-null   int64
 5   DiffBreath     2575 non-null   int64
 6   InfectionProb  2575 non-null   int64
dtypes: int64(7)
memory usage: 140.9 KB

covid.describe(include='all')

	S.No	Fever	BodyPain	Age	RunnyNose	DiffBreath	InfectionProb
count	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000
mean	1288.000000	100.969709	0.492816	51.023301	0.502136	0.002330	0.493592
std	743.482795	1.999771	0.500045	29.014442	0.500093	0.816969	0.500056
min	1.000000	98.000000	0.000000	1.000000	0.000000	-1.000000	0.000000
25%	644.500000	99.000000	0.000000	26.000000	0.000000	-1.000000	0.000000
50%	1288.000000	101.000000	0.000000	50.000000	1.000000	0.000000	0.000000
75%	1931.500000	103.000000	1.000000	76.500000	1.000000	1.000000	1.000000
max	2575.000000	104.000000	1.000000	100.000000	1.000000	1.000000	1.000000

covid.columns

Index(['S.No', 'Fever', 'BodyPain', 'Age', 'RunnyNose', 'DiffBreath',
       'InfectionProb'],
      dtype='object')

Finding Missing Value

plot_missing(covid)

  0%|          | 0/118 [00:00<?, ?it/s]

Stats Bar Chart Spectrum Heat Map Dendrogram

Missing Statistics

Missing Cells	0
Missing Cells (%)	0.0%
Missing Columns	0
Missing Rows	0
Avg Missing Cells per Column	0.0
Avg Missing Cells per Row	0.0

'height': 500

Height of the plot

'width': 500

Width of the plot

Copy All Parameters

'spectrum.bins': 20

Number of bins

'height': 500

Height of the plot

'width': 500

Width of the plot

Copy All Parameters

'height': 500

Height of the plot

'width': 500

Width of the plot

Copy All Parameters

'height': 500

Height of the plot

'width': 500

Width of the plot

Copy All Parameters

# create a table with data missing 
missing_values=covid.isnull().sum() # missing values

percent_missing = covid.isnull().sum()/covid.shape[0]*100 # missing value %

value = {
    'missing_values ':missing_values,
    'percent_missing %':percent_missing  
}
frame=pd.DataFrame(value)
frame

	missing_values	percent_missing %
S.No	0	0.0
Fever	0	0.0
BodyPain	0	0.0
Age	0	0.0
RunnyNose	0	0.0
DiffBreath	0	0.0
InfectionProb	0	0.0

Data Vizualisation

Infection Probability (Target)

sns.countplot(x='InfectionProb',data=covid)

<AxesSubplot:xlabel='InfectionProb', ylabel='count'>

Fever Problem

sns.countplot(x='Fever', data=covid)

<AxesSubplot:xlabel='Fever', ylabel='count'>

covid["Fever"].value_counts().plot.pie(explode=[0.1,0.1,0.1,0.1,0.1,0.1,0.1],autopct='%1.1f%%',shadow=True)
plt.title('Number of Cases');

Body Pain Chick List Problem

sns.countplot(x='BodyPain', hue='InfectionProb', data=covid)

<AxesSubplot:xlabel='BodyPain', ylabel='count'>

covid["BodyPain"].value_counts().plot.pie(explode=[0.1,0.1],autopct='%1.1f%%',shadow=True)
plt.title('Number of Cases')

Text(0.5, 1.0, 'Number of Cases')

Age with Infection Probability Check List

sns.countplot(x='Age', hue='InfectionProb', data=covid)

<AxesSubplot:xlabel='Age', ylabel='count'>

Runny Nose with Infection Probability Checklist Prob

sns.countplot(x='RunnyNose', hue='InfectionProb', data=covid)

<AxesSubplot:xlabel='RunnyNose', ylabel='count'>

covid["RunnyNose"].value_counts().plot.pie(explode=[0.1,0.1],autopct='%1.1f%%',shadow=True)
plt.title('Number of Cases');

Diff Breath With Infection Probability Prob

sns.countplot(x='DiffBreath', hue='InfectionProb', data=covid)

<AxesSubplot:xlabel='DiffBreath', ylabel='count'>

covid["DiffBreath"].value_counts().plot.pie(explode=[0.1,0.1,0.1],autopct='%1.1f%%',shadow=True)
plt.title('Number of Cases');

Feature Transformation

from sklearn.preprocessing import LabelEncoder
e=LabelEncoder()

covid['Fever']=e.fit_transform(covid['Fever'])
covid['BodyPain']=e.fit_transform(covid['BodyPain'])
covid['Age']=e.fit_transform(covid['Age'])
covid['RunnyNose']=e.fit_transform(covid['RunnyNose'])
covid['DiffBreath']=e.fit_transform(covid['DiffBreath'])
covid['InfectionProb']=e.fit_transform(covid['InfectionProb'])

covid.head()

	S.No	Fever	BodyPain	Age	RunnyNose	DiffBreath	InfectionProb
0	1	4	0	8	0	0	0
1	2	4	0	9	0	1	1
2	3	6	0	32	1	0	0
3	4	3	1	58	0	2	0
4	5	1	0	97	0	1	0

covid.dtypes.value_counts()

int64    7
dtype: int64

Info About Our Data After Transformation

covid.describe(include='all')

	S.No	Fever	BodyPain	Age	RunnyNose	DiffBreath	InfectionProb
count	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000	2575.000000
mean	1288.000000	2.969709	0.492816	50.023301	0.502136	1.002330	0.493592
std	743.482795	1.999771	0.500045	29.014442	0.500093	0.816969	0.500056
min	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	644.500000	1.000000	0.000000	25.000000	0.000000	0.000000	0.000000
50%	1288.000000	3.000000	0.000000	49.000000	1.000000	1.000000	0.000000
75%	1931.500000	5.000000	1.000000	75.500000	1.000000	2.000000	1.000000
max	2575.000000	6.000000	1.000000	99.000000	1.000000	2.000000	1.000000

covid.hist(figsize=(15,10));

Correlation Betwenn Features

plot_correlation(covid)

Stats Pearson Spearman KendallTau

	Pearson	Spearman	KendallTau
Highest Positive Correlation	0.048	0.048	0.045
Highest Negative Correlation	-0.036	-0.036	-0.036
Lowest Correlation	0.0	0.0	0.0
Mean Correlation	-0.002	-0.002	-0.003

'height': 400

Height of the plot

'width': 400

Width of the plot

Copy All Parameters

• Most positive correlated: (RunnyNose, DiffBreath)
• Most negative correlated: (BodyPain, RunnyNose)
• Least correlated: (Fever, DiffBreath)

'height': 400

Height of the plot

'width': 400

Width of the plot

Copy All Parameters

• Most positive correlated: (RunnyNose, DiffBreath)
• Most negative correlated: (BodyPain, RunnyNose)
• Least correlated: (Fever, DiffBreath)

'height': 400

Height of the plot

'width': 400

Width of the plot

Copy All Parameters

• Most positive correlated: (RunnyNose, DiffBreath)
• Most negative correlated: (BodyPain, RunnyNose)
• Least correlated: (Fever, DiffBreath)

corr=covid.corr()
corr.style.background_gradient(cmap='coolwarm',axis=None)

	S.No	Fever	BodyPain	Age	RunnyNose	DiffBreath	InfectionProb
S.No	1.000000	0.033867	-0.030738	0.008658	0.016956	-0.015748	0.030006
Fever	0.033867	1.000000	-0.013039	-0.021883	0.011719	0.000281	-0.007187
BodyPain	-0.030738	-0.013039	1.000000	-0.012199	-0.036059	0.005747	-0.031646
Age	0.008658	-0.021883	-0.012199	1.000000	0.029824	0.006668	-0.035496
RunnyNose	0.016956	0.011719	-0.036059	0.029824	1.000000	0.047533	-0.017423
DiffBreath	-0.015748	0.000281	0.005747	0.006668	0.047533	1.000000	-0.027542
InfectionProb	0.030006	-0.007187	-0.031646	-0.035496	-0.017423	-0.027542	1.000000

Machine Learning Algo

from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler

x=covid.drop('InfectionProb',axis=1)
y=covid['InfectionProb']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20)

SVM Support Vector Machine

from sklearn import svm
#Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel
#Train the model using the training sets
clf.fit(x_train, y_train)
#Predict the response for test dataset
y_pred = clf.predict(x_test)
#Score/Accuracy
from sklearn.metrics import confusion_matrix, classification_report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

[[120 138]
 [124 133]]
              precision    recall  f1-score   support

           0       0.49      0.47      0.48       258
           1       0.49      0.52      0.50       257

    accuracy                           0.49       515
   macro avg       0.49      0.49      0.49       515
weighted avg       0.49      0.49      0.49       515

#Import svm model
from sklearn import svm
#Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel
#Train the model using the training sets
clf.fit(x_train, y_train)
#Predict the response for test dataset
y_pred = clf.predict(x_test)
#Score/Accuracy
acc_svc=clf.score(x_test, y_test)*100
acc_svc

49.12621359223301

Logistic Regression

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
#Fit the model
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
#Score/Accuracy
acc_logreg=model.score(x_test, y_test)*100
acc_logreg

48.932038834951456

K_Neighbors_Classifier

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=20)
knn.fit(x_train, y_train)
y_pred = knn.predict(x_test)
#Score/Accuracy
acc_knn=knn.score(x_test, y_test)*100
acc_knn

C:\Users\GC\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\GC\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)

48.932038834951456

DecisionTreeClassifier

from sklearn import tree
t = tree.DecisionTreeClassifier()
t.fit(x_train,y_train)
y_pred = t.predict(x_test)
#Score/Accuracy
acc_decisiontree=t.score(x_test, y_test)*100
acc_decisiontree

53.398058252427184

Naive_Bayes

from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(x_train,y_train)
#Score/Accuracy
acc_gaussian= model.score(x_test, y_test)*100
acc_gaussian

48.349514563106794

Score DataFrame

models = pd.DataFrame({
    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', 
               'Naive Bayes',   
              'Decision Tree'],
    'Score': [acc_svc, acc_knn, acc_logreg,  acc_gaussian, acc_decisiontree]})
models.sort_values(by='Score', ascending=False)

	Model	Score
4	Decision Tree	53.398058
0	Support Vector Machines	49.126214
1	KNN	48.932039
2	Logistic Regression	48.932039
3	Naive Bayes	48.349515